# Adapting to unknown noise level in sparse deconvolution

arXiv: https://arxiv.org/abs/1606.04760 · HTML: https://mlsanity.com/paper/1606.04760/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2016.

## Built on by (most cited first, 4 of 4)

- [Off-the-grid learning of mixtures from a continuous dictionary](https://mlsanity.com/paper/2207.00171/) (2022)
- [Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary](https://mlsanity.com/paper/2210.16311/) (2022)
- [Off-the-grid prediction and testing for linear combination of translated features](https://mlsanity.com/paper/2212.01169/) (2022)
- [Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures](https://mlsanity.com/paper/2507.08444/) (2025)

## Abstract (arXiv)

In this paper, we study sparse spike deconvolution over the space of complex-valued measures when the input measure is a finite sum of Dirac masses. We introduce a modified version of the Beurling Lasso (BLasso), a semi-definite program that we refer to as the Concomitant Beurling Lasso (CBLasso). This new procedure estimates the target measure and the unknown noise level simultaneously. Contrary to previous estimators in the literature, theory holds for a tuning parameter that depends only on the sample size, so that it can be used for unknown noise level problems. Consistent noise level estimation is standardly proved. As for Radon measure estimation, theoretical guarantees match the previous state-of-the-art results in Super-Resolution regarding minimax prediction and localization. The proofs are based on a bound on the noise level given by a new tail estimate of the supremum of a stationary non-Gaussian process through the Rice method.